Overview

Dataset statistics

Number of variables28
Number of observations56
Missing cells24
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.4 KiB
Average record size in memory226.3 B

Variable types

Numeric9
Categorical19

Alerts

airdate has constant value "2020-12-06" Constant
url has a high cardinality: 56 distinct values High cardinality
name has a high cardinality: 53 distinct values High cardinality
_links_self_href has a high cardinality: 56 distinct values High cardinality
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
season is highly correlated with number and 2 other fieldsHigh correlation
number is highly correlated with seasonHigh correlation
runtime is highly correlated with season and 2 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with season and 2 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with _embedded_show_summary and 9 other fieldsHigh correlation
summary is highly correlated with _embedded_show_summary and 3 other fieldsHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
_embedded_show_type is highly correlated with url and 3 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_summary and 5 other fieldsHigh correlation
url is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_summary and 6 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
name is highly correlated with url and 2 other fieldsHigh correlation
airdate is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_summary and 6 other fieldsHigh correlation
_links_self_href is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
type is highly correlated with _embedded_show_summary and 6 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
_embedded_show_language is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_summary and 8 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
id is highly correlated with url and 16 other fieldsHigh correlation
url is highly correlated with id and 25 other fieldsHigh correlation
name is highly correlated with id and 19 other fieldsHigh correlation
season is highly correlated with id and 15 other fieldsHigh correlation
number is highly correlated with id and 14 other fieldsHigh correlation
type is highly correlated with url and 11 other fieldsHigh correlation
airtime is highly correlated with url and 20 other fieldsHigh correlation
airstamp is highly correlated with id and 19 other fieldsHigh correlation
runtime is highly correlated with url and 19 other fieldsHigh correlation
summary is highly correlated with id and 15 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 20 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_type is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_language is highly correlated with url and 20 other fieldsHigh correlation
_embedded_show_genres is highly correlated with url and 16 other fieldsHigh correlation
_embedded_show_status is highly correlated with url and 13 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_ended is highly correlated with url and 11 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_weight is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with url and 13 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_updated is highly correlated with url and 15 other fieldsHigh correlation
_links_self_href is highly correlated with id and 25 other fieldsHigh correlation
number has 2 (3.6%) missing values Missing
runtime has 6 (10.7%) missing values Missing
_embedded_show_runtime has 12 (21.4%) missing values Missing
_embedded_show_averageRuntime has 4 (7.1%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique

Reproduction

Analysis started2022-05-10 02:02:53.201848
Analysis finished2022-05-10 02:03:23.947715
Duration30.75 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2029711.446
Minimum1956338
Maximum2318098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-05-09T21:03:24.040694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1956338
5-th percentile1965553
Q11977297.75
median1981699.5
Q32044187
95-th percentile2240011.75
Maximum2318098
Range361760
Interquartile range (IQR)66889.25

Descriptive statistics

Standard deviation91774.99906
Coefficient of variation (CV)0.04521578632
Kurtosis1.771050666
Mean2029711.446
Median Absolute Deviation (MAD)10419
Skewness1.684405582
Sum113663841
Variance8422650452
MonotonicityNot monotonic
2022-05-09T21:03:24.210363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22698001
 
1.8%
19563381
 
1.8%
19644461
 
1.8%
19809321
 
1.8%
19702531
 
1.8%
19782611
 
1.8%
19826251
 
1.8%
19826261
 
1.8%
19826271
 
1.8%
19972981
 
1.8%
Other values (46)46
82.1%
ValueCountFrequency (%)
19563381
1.8%
19570671
1.8%
19644461
1.8%
19659221
1.8%
19677551
1.8%
19692271
1.8%
19692471
1.8%
19702531
1.8%
19723081
1.8%
19740491
1.8%
ValueCountFrequency (%)
23180981
1.8%
22698001
1.8%
22559831
1.8%
22346881
1.8%
21955991
1.8%
21785611
1.8%
21761231
1.8%
21659291
1.8%
21389241
1.8%
21168311
1.8%

url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size576.0 B
https://www.tvmaze.com/episodes/2269800/manaki-1x07-golos-zvera-delo-arhangelskogo-manaka
 
1
https://www.tvmaze.com/episodes/1956338/hero-return-1x09-episode-9
 
1
https://www.tvmaze.com/episodes/1964446/bani-negri-pentru-zile-albe-1x03-socoteala
 
1
https://www.tvmaze.com/episodes/1980932/cuzie-pisma-1x20-kak-sdelat-kaming-aut-stavki-zlo-pocemu-on-ohladel
 
1
https://www.tvmaze.com/episodes/1970253/amore-1x49-truth-will-prevail-12
 
1
Other values (51)
51 

Length

Max length146
Median length98
Mean length83.96428571
Min length61

Characters and Unicode

Total characters4702
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/2269800/manaki-1x07-golos-zvera-delo-arhangelskogo-manaka
2nd rowhttps://www.tvmaze.com/episodes/1956338/hero-return-1x09-episode-9
3rd rowhttps://www.tvmaze.com/episodes/1978217/swallowed-star-1x03-episode-3
4th rowhttps://www.tvmaze.com/episodes/2052506/wu-shen-zhu-zai-1x81-episode-81
5th rowhttps://www.tvmaze.com/episodes/2138924/tokyo-joshi-pro-wrestling-2020-12-06-tjpw-fall-tour-20-womm-wrestling-of-my-mind

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2269800/manaki-1x07-golos-zvera-delo-arhangelskogo-manaka1
 
1.8%
https://www.tvmaze.com/episodes/1956338/hero-return-1x09-episode-91
 
1.8%
https://www.tvmaze.com/episodes/1964446/bani-negri-pentru-zile-albe-1x03-socoteala1
 
1.8%
https://www.tvmaze.com/episodes/1980932/cuzie-pisma-1x20-kak-sdelat-kaming-aut-stavki-zlo-pocemu-on-ohladel1
 
1.8%
https://www.tvmaze.com/episodes/1970253/amore-1x49-truth-will-prevail-121
 
1.8%
https://www.tvmaze.com/episodes/1978261/ultra-galaxy-fight-the-absolute-conspiracy-1x03-part-31
 
1.8%
https://www.tvmaze.com/episodes/1982625/pappas-pojkar-1x01-obekvam-striptease-och-en-skrikande-bebis1
 
1.8%
https://www.tvmaze.com/episodes/1982626/pappas-pojkar-1x02-dagsfylla-men-var-ar-festen1
 
1.8%
https://www.tvmaze.com/episodes/1982627/pappas-pojkar-1x03-herman-forsoker-bli-bedragare1
 
1.8%
https://www.tvmaze.com/episodes/1997298/the-george-lucas-talk-show-1x18-episode-xviii-decembyrinth1
 
1.8%
Other values (46)46
82.1%

Length

2022-05-09T21:03:24.378480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2269800/manaki-1x07-golos-zvera-delo-arhangelskogo-manaka1
 
1.8%
https://www.tvmaze.com/episodes/1956338/hero-return-1x09-episode-91
 
1.8%
https://www.tvmaze.com/episodes/1981696/meisje-van-plezier-3x04-aflevering-41
 
1.8%
https://www.tvmaze.com/episodes/1978217/swallowed-star-1x03-episode-31
 
1.8%
https://www.tvmaze.com/episodes/2052506/wu-shen-zhu-zai-1x81-episode-811
 
1.8%
https://www.tvmaze.com/episodes/2138924/tokyo-joshi-pro-wrestling-2020-12-06-tjpw-fall-tour-20-womm-wrestling-of-my-mind1
 
1.8%
https://www.tvmaze.com/episodes/2012320/mans-diary-2x05-episode-51
 
1.8%
https://www.tvmaze.com/episodes/1957067/atlantic-crossing-1x07-gaven1
 
1.8%
https://www.tvmaze.com/episodes/1977314/stjernestov-1x06-episode-61
 
1.8%
https://www.tvmaze.com/episodes/1974049/love-revolution-1x23-episode-231
 
1.8%
Other values (46)46
82.1%

Most occurring characters

ValueCountFrequency (%)
e390
 
8.3%
-369
 
7.8%
s298
 
6.3%
/280
 
6.0%
t260
 
5.5%
o236
 
5.0%
a225
 
4.8%
i198
 
4.2%
w194
 
4.1%
p186
 
4.0%
Other values (29)2066
43.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3231
68.7%
Decimal Number654
 
13.9%
Other Punctuation448
 
9.5%
Dash Punctuation369
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e390
12.1%
s298
 
9.2%
t260
 
8.0%
o236
 
7.3%
a225
 
7.0%
i198
 
6.1%
w194
 
6.0%
p186
 
5.8%
m175
 
5.4%
d115
 
3.6%
Other values (15)954
29.5%
Decimal Number
ValueCountFrequency (%)
1126
19.3%
2110
16.8%
098
15.0%
976
11.6%
651
7.8%
343
 
6.6%
841
 
6.3%
741
 
6.3%
535
 
5.4%
433
 
5.0%
Other Punctuation
ValueCountFrequency (%)
/280
62.5%
.112
 
25.0%
:56
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-369
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3231
68.7%
Common1471
31.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e390
12.1%
s298
 
9.2%
t260
 
8.0%
o236
 
7.3%
a225
 
7.0%
i198
 
6.1%
w194
 
6.0%
p186
 
5.8%
m175
 
5.4%
d115
 
3.6%
Other values (15)954
29.5%
Common
ValueCountFrequency (%)
-369
25.1%
/280
19.0%
1126
 
8.6%
.112
 
7.6%
2110
 
7.5%
098
 
6.7%
976
 
5.2%
:56
 
3.8%
651
 
3.5%
343
 
2.9%
Other values (4)150
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e390
 
8.3%
-369
 
7.8%
s298
 
6.3%
/280
 
6.0%
t260
 
5.5%
o236
 
5.0%
a225
 
4.8%
i198
 
4.2%
w194
 
4.1%
p186
 
4.0%
Other values (29)2066
43.9%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct53
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size576.0 B
Episode 5
 
3
Episode 6
 
2
Голос зверя. Дело архангельского маньяка
 
1
Feelings are Hard to Navigate, Especially in Space
 
1
Part 3
 
1
Other values (48)
48 

Length

Max length95
Median length53
Mean length22.26785714
Min length5

Characters and Unicode

Total characters1247
Distinct characters121
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)91.1%

Sample

1st rowГолос зверя. Дело архангельского маньяка
2nd rowEpisode 9
3rd rowEpisode 3
4th rowEpisode 81
5th rowTJPW Fall Tour '20 ~ WOMM (Wrestling Of My Mind) ~

Common Values

ValueCountFrequency (%)
Episode 53
 
5.4%
Episode 62
 
3.6%
Голос зверя. Дело архангельского маньяка1
 
1.8%
Feelings are Hard to Navigate, Especially in Space1
 
1.8%
Part 31
 
1.8%
Obekväm striptease (och en skrikande bebis)1
 
1.8%
Dagsfylla!! Men var är festen?1
 
1.8%
Herman försöker bli bedragare1
 
1.8%
Episode XVIII: DECEMBYRINTH1
 
1.8%
GRA-ŽU-LIS ir mėgstamiausios lietuvių Covid-19 melagienos1
 
1.8%
Other values (43)43
76.8%

Length

2022-05-09T21:03:24.524037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode16
 
7.8%
4
 
2.0%
aflevering4
 
2.0%
in3
 
1.5%
63
 
1.5%
53
 
1.5%
23
 
1.5%
of3
 
1.5%
43
 
1.5%
33
 
1.5%
Other values (154)159
77.9%

Most occurring characters

ValueCountFrequency (%)
148
 
11.9%
e84
 
6.7%
i61
 
4.9%
a51
 
4.1%
s50
 
4.0%
r45
 
3.6%
o43
 
3.4%
l35
 
2.8%
n35
 
2.8%
t33
 
2.6%
Other values (111)662
53.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter802
64.3%
Uppercase Letter178
 
14.3%
Space Separator148
 
11.9%
Decimal Number59
 
4.7%
Other Punctuation40
 
3.2%
Dash Punctuation8
 
0.6%
Close Punctuation4
 
0.3%
Open Punctuation3
 
0.2%
Currency Symbol3
 
0.2%
Math Symbol2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e84
 
10.5%
i61
 
7.6%
a51
 
6.4%
s50
 
6.2%
r45
 
5.6%
o43
 
5.4%
l35
 
4.4%
n35
 
4.4%
t33
 
4.1%
d29
 
3.6%
Other values (48)336
41.9%
Uppercase Letter
ValueCountFrequency (%)
E27
 
15.2%
T15
 
8.4%
S11
 
6.2%
A10
 
5.6%
M10
 
5.6%
C8
 
4.5%
W8
 
4.5%
O7
 
3.9%
N6
 
3.4%
P6
 
3.4%
Other values (25)70
39.3%
Decimal Number
ValueCountFrequency (%)
214
23.7%
011
18.6%
19
15.3%
66
10.2%
45
 
8.5%
34
 
6.8%
53
 
5.1%
93
 
5.1%
72
 
3.4%
82
 
3.4%
Other Punctuation
ValueCountFrequency (%)
,8
20.0%
"6
15.0%
.5
12.5%
:4
10.0%
/4
10.0%
?4
10.0%
#3
 
7.5%
!3
 
7.5%
'2
 
5.0%
@1
 
2.5%
Currency Symbol
ValueCountFrequency (%)
$1
33.3%
1
33.3%
¥1
33.3%
Space Separator
ValueCountFrequency (%)
148
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%
Close Punctuation
ValueCountFrequency (%)
)4
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Math Symbol
ValueCountFrequency (%)
~2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin783
62.8%
Common267
 
21.4%
Cyrillic197
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e84
 
10.7%
i61
 
7.8%
a51
 
6.5%
s50
 
6.4%
r45
 
5.7%
o43
 
5.5%
l35
 
4.5%
n35
 
4.5%
t33
 
4.2%
d29
 
3.7%
Other values (44)317
40.5%
Cyrillic
ValueCountFrequency (%)
о22
 
11.2%
а18
 
9.1%
к13
 
6.6%
л12
 
6.1%
е12
 
6.1%
н10
 
5.1%
м9
 
4.6%
и8
 
4.1%
с7
 
3.6%
р7
 
3.6%
Other values (29)79
40.1%
Common
ValueCountFrequency (%)
148
55.4%
214
 
5.2%
011
 
4.1%
19
 
3.4%
-8
 
3.0%
,8
 
3.0%
66
 
2.2%
"6
 
2.2%
45
 
1.9%
.5
 
1.9%
Other values (18)47
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1039
83.3%
Cyrillic197
 
15.8%
None10
 
0.8%
Currency Symbols1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
148
 
14.2%
e84
 
8.1%
i61
 
5.9%
a51
 
4.9%
s50
 
4.8%
r45
 
4.3%
o43
 
4.1%
l35
 
3.4%
n35
 
3.4%
t33
 
3.2%
Other values (63)454
43.7%
Cyrillic
ValueCountFrequency (%)
о22
 
11.2%
а18
 
9.1%
к13
 
6.6%
л12
 
6.1%
е12
 
6.1%
н10
 
5.1%
м9
 
4.6%
и8
 
4.1%
с7
 
3.6%
р7
 
3.6%
Other values (29)79
40.1%
None
ValueCountFrequency (%)
ö2
20.0%
ä2
20.0%
Ž1
10.0%
å1
10.0%
í1
10.0%
ė1
10.0%
ų1
10.0%
¥1
10.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.0892857
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-05-09T21:03:24.660146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile2020
Maximum2020
Range2019
Interquartile range (IQR)2

Descriptive statistics

Standard deviation629.5324752
Coefficient of variation (CV)2.873406032
Kurtosis4.991799558
Mean219.0892857
Median Absolute Deviation (MAD)1
Skewness2.61028705
Sum12269
Variance396311.1373
MonotonicityNot monotonic
2022-05-09T21:03:24.766370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
127
48.2%
29
 
16.1%
39
 
16.1%
20206
 
10.7%
53
 
5.4%
481
 
1.8%
141
 
1.8%
ValueCountFrequency (%)
127
48.2%
29
 
16.1%
39
 
16.1%
53
 
5.4%
141
 
1.8%
481
 
1.8%
20206
 
10.7%
ValueCountFrequency (%)
20206
 
10.7%
481
 
1.8%
141
 
1.8%
53
 
5.4%
39
 
16.1%
29
 
16.1%
127
48.2%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)48.1%
Missing2
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean20.90740741
Minimum1
Maximum333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-05-09T21:03:24.885026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.65
Q13
median7
Q317.5
95-th percentile60.85
Maximum333
Range332
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation47.49389971
Coefficient of variation (CV)2.271630279
Kurtosis36.37004185
Mean20.90740741
Median Absolute Deviation (MAD)5
Skewness5.636585817
Sum1129
Variance2255.67051
MonotonicityNot monotonic
2022-05-09T21:03:25.015063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
27
12.5%
36
 
10.7%
75
 
8.9%
54
 
7.1%
63
 
5.4%
43
 
5.4%
13
 
5.4%
492
 
3.6%
422
 
3.6%
82
 
3.6%
Other values (16)17
30.4%
ValueCountFrequency (%)
13
5.4%
27
12.5%
36
10.7%
43
5.4%
54
7.1%
63
5.4%
75
8.9%
82
 
3.6%
91
 
1.8%
102
 
3.6%
ValueCountFrequency (%)
3331
1.8%
851
1.8%
811
1.8%
501
1.8%
492
3.6%
461
1.8%
422
3.6%
361
1.8%
351
1.8%
231
1.8%

type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size576.0 B
regular
54 
insignificant_special
 
1
significant_special
 
1

Length

Max length21
Median length7
Mean length7.464285714
Min length7

Characters and Unicode

Total characters418
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)3.6%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular54
96.4%
insignificant_special1
 
1.8%
significant_special1
 
1.8%

Length

2022-05-09T21:03:25.133632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:03:25.257417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular54
96.4%
insignificant_special1
 
1.8%
significant_special1
 
1.8%

Most occurring characters

ValueCountFrequency (%)
r108
25.8%
a58
13.9%
e56
13.4%
g56
13.4%
l56
13.4%
u54
12.9%
i9
 
2.2%
n5
 
1.2%
s4
 
1.0%
c4
 
1.0%
Other values (4)8
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter416
99.5%
Connector Punctuation2
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r108
26.0%
a58
13.9%
e56
13.5%
g56
13.5%
l56
13.5%
u54
13.0%
i9
 
2.2%
n5
 
1.2%
s4
 
1.0%
c4
 
1.0%
Other values (3)6
 
1.4%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin416
99.5%
Common2
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r108
26.0%
a58
13.9%
e56
13.5%
g56
13.5%
l56
13.5%
u54
13.0%
i9
 
2.2%
n5
 
1.2%
s4
 
1.0%
c4
 
1.0%
Other values (3)6
 
1.4%
Common
ValueCountFrequency (%)
_2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r108
25.8%
a58
13.9%
e56
13.4%
g56
13.4%
l56
13.4%
u54
12.9%
i9
 
2.2%
n5
 
1.2%
s4
 
1.0%
c4
 
1.0%
Other values (4)8
 
1.9%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size576.0 B
2020-12-06
56 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters560
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-06
2nd row2020-12-06
3rd row2020-12-06
4th row2020-12-06
5th row2020-12-06

Common Values

ValueCountFrequency (%)
2020-12-0656
100.0%

Length

2022-05-09T21:03:25.358463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:03:25.466222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-0656
100.0%

Most occurring characters

ValueCountFrequency (%)
2168
30.0%
0168
30.0%
-112
20.0%
156
 
10.0%
656
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number448
80.0%
Dash Punctuation112
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2168
37.5%
0168
37.5%
156
 
12.5%
656
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common560
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2168
30.0%
0168
30.0%
-112
20.0%
156
 
10.0%
656
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2168
30.0%
0168
30.0%
-112
20.0%
156
 
10.0%
656
 
10.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size576.0 B
nan
42 
10:00
 
3
12:00
 
3
06:00
 
2
17:00
 
1
Other values (5)

Length

Max length5
Median length3
Mean length3.5
Min length3

Characters and Unicode

Total characters196
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)10.7%

Sample

1st rownan
2nd row10:00
3rd row10:00
4th row10:00
5th row12:00

Common Values

ValueCountFrequency (%)
nan42
75.0%
10:003
 
5.4%
12:003
 
5.4%
06:002
 
3.6%
17:001
 
1.8%
18:001
 
1.8%
14:001
 
1.8%
22:201
 
1.8%
19:001
 
1.8%
20:001
 
1.8%

Length

2022-05-09T21:03:25.562436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:03:25.715134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan42
75.0%
10:003
 
5.4%
12:003
 
5.4%
06:002
 
3.6%
17:001
 
1.8%
18:001
 
1.8%
14:001
 
1.8%
22:201
 
1.8%
19:001
 
1.8%
20:001
 
1.8%

Most occurring characters

ValueCountFrequency (%)
n84
42.9%
a42
21.4%
033
 
16.8%
:14
 
7.1%
110
 
5.1%
27
 
3.6%
62
 
1.0%
71
 
0.5%
81
 
0.5%
41
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter126
64.3%
Decimal Number56
28.6%
Other Punctuation14
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
033
58.9%
110
 
17.9%
27
 
12.5%
62
 
3.6%
71
 
1.8%
81
 
1.8%
41
 
1.8%
91
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
n84
66.7%
a42
33.3%
Other Punctuation
ValueCountFrequency (%)
:14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin126
64.3%
Common70
35.7%

Most frequent character per script

Common
ValueCountFrequency (%)
033
47.1%
:14
20.0%
110
 
14.3%
27
 
10.0%
62
 
2.9%
71
 
1.4%
81
 
1.4%
41
 
1.4%
91
 
1.4%
Latin
ValueCountFrequency (%)
n84
66.7%
a42
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n84
42.9%
a42
21.4%
033
 
16.8%
:14
 
7.1%
110
 
5.1%
27
 
3.6%
62
 
1.0%
71
 
0.5%
81
 
0.5%
41
 
0.5%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size576.0 B
2020-12-06T12:00:00+00:00
21 
2020-12-06T11:00:00+00:00
15 
2020-12-06T17:00:00+00:00
2020-12-06T02:00:00+00:00
2020-12-06T05:00:00+00:00
 
2
Other values (9)

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters1400
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)16.1%

Sample

1st row2020-12-06T00:00:00+00:00
2nd row2020-12-06T02:00:00+00:00
3rd row2020-12-06T02:00:00+00:00
4th row2020-12-06T02:00:00+00:00
5th row2020-12-06T03:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-06T12:00:00+00:0021
37.5%
2020-12-06T11:00:00+00:0015
26.8%
2020-12-06T17:00:00+00:006
 
10.7%
2020-12-06T02:00:00+00:003
 
5.4%
2020-12-06T05:00:00+00:002
 
3.6%
2020-12-06T00:00:00+00:001
 
1.8%
2020-12-06T03:00:00+00:001
 
1.8%
2020-12-06T04:00:00+00:001
 
1.8%
2020-12-06T08:00:00+00:001
 
1.8%
2020-12-06T09:00:00+00:001
 
1.8%
Other values (4)4
 
7.1%

Length

2022-05-09T21:03:25.834636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-06t12:00:00+00:0021
37.5%
2020-12-06t11:00:00+00:0015
26.8%
2020-12-06t17:00:00+00:006
 
10.7%
2020-12-06t02:00:00+00:003
 
5.4%
2020-12-06t05:00:00+00:002
 
3.6%
2020-12-06t00:00:00+00:001
 
1.8%
2020-12-06t03:00:00+00:001
 
1.8%
2020-12-06t04:00:00+00:001
 
1.8%
2020-12-06t08:00:00+00:001
 
1.8%
2020-12-06t09:00:00+00:001
 
1.8%
Other values (4)4
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0629
44.9%
2194
 
13.9%
:168
 
12.0%
1116
 
8.3%
-112
 
8.0%
T56
 
4.0%
+56
 
4.0%
654
 
3.9%
78
 
0.6%
52
 
0.1%
Other values (4)5
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1008
72.0%
Other Punctuation168
 
12.0%
Dash Punctuation112
 
8.0%
Uppercase Letter56
 
4.0%
Math Symbol56
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0629
62.4%
2194
 
19.2%
1116
 
11.5%
654
 
5.4%
78
 
0.8%
52
 
0.2%
42
 
0.2%
31
 
0.1%
81
 
0.1%
91
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:168
100.0%
Dash Punctuation
ValueCountFrequency (%)
-112
100.0%
Uppercase Letter
ValueCountFrequency (%)
T56
100.0%
Math Symbol
ValueCountFrequency (%)
+56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1344
96.0%
Latin56
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0629
46.8%
2194
 
14.4%
:168
 
12.5%
1116
 
8.6%
-112
 
8.3%
+56
 
4.2%
654
 
4.0%
78
 
0.6%
52
 
0.1%
42
 
0.1%
Other values (3)3
 
0.2%
Latin
ValueCountFrequency (%)
T56
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0629
44.9%
2194
 
13.9%
:168
 
12.0%
1116
 
8.3%
-112
 
8.0%
T56
 
4.0%
+56
 
4.0%
654
 
3.9%
78
 
0.6%
52
 
0.1%
Other values (4)5
 
0.4%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)54.0%
Missing6
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean47.74
Minimum5
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-05-09T21:03:25.965000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.9
Q122
median43.5
Q345
95-th percentile120
Maximum188
Range183
Interquartile range (IQR)23

Descriptive statistics

Standard deviation39.49001941
Coefficient of variation (CV)0.8271893466
Kurtosis4.591702338
Mean47.74
Median Absolute Deviation (MAD)16.5
Skewness2.086217074
Sum2387
Variance1559.461633
MonotonicityNot monotonic
2022-05-09T21:03:26.090963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4511
19.6%
1204
 
7.1%
153
 
5.4%
603
 
5.4%
223
 
5.4%
402
 
3.6%
422
 
3.6%
442
 
3.6%
302
 
3.6%
631
 
1.8%
Other values (17)17
30.4%
(Missing)6
 
10.7%
ValueCountFrequency (%)
51
 
1.8%
81
 
1.8%
101
 
1.8%
121
 
1.8%
141
 
1.8%
153
5.4%
181
 
1.8%
201
 
1.8%
211
 
1.8%
223
5.4%
ValueCountFrequency (%)
1881
 
1.8%
1801
 
1.8%
1204
 
7.1%
631
 
1.8%
621
 
1.8%
603
 
5.4%
541
 
1.8%
4511
19.6%
442
 
3.6%
431
 
1.8%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size576.0 B
nan
47 
<p>Märtha ends up in a quandary when the president announces that he wants to support Norway and asks her to say how she feels about him.</p>
 
1
<p>When a local teenager suddenly disappears, Elena explores Father Vergara's possible connection. As more untimely deaths befall the town, Marquess Roque presents Elena with an enticing job offer, and Merche urges Paco to keep his distance.</p>
 
1
<p>Monica follows up a lead given to her at a party, visiting the co-workers of a man they describe as a pervert.</p>
 
1
<p>As the death toll rises, THE GEORGE LUCAS TALK SHOW continues to deliver quality livestream content from the digital safety of PLANET SCUM. Retired filmmaker GEORGE LUCAS (notoriously dead-eyed character actor CONNOR RATLIFF) and his CGI co-host WATTO (GRIFFIN NEWMAN, co-lead on Amazon's THE TICK) welcome guests and have fun while actively NOT spreading a disease. They are joined, as always, by snack-snacking, nap-napping producer PATRICK COTNOIR (aka Nickname Jokenoir) whose druthers include you following him on twitter RIGHT NOW: @patrickcotnoir Only a few weeks left in 2020. Who knows what fresh hell awaits us in 2021? Dot dot dot dot..</p>
 
1
Other values (5)

Length

Max length654
Median length3
Mean length46.80357143
Min length3

Characters and Unicode

Total characters2621
Distinct characters73
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)16.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan47
83.9%
<p>Märtha ends up in a quandary when the president announces that he wants to support Norway and asks her to say how she feels about him.</p>1
 
1.8%
<p>When a local teenager suddenly disappears, Elena explores Father Vergara's possible connection. As more untimely deaths befall the town, Marquess Roque presents Elena with an enticing job offer, and Merche urges Paco to keep his distance.</p>1
 
1.8%
<p>Monica follows up a lead given to her at a party, visiting the co-workers of a man they describe as a pervert.</p>1
 
1.8%
<p>As the death toll rises, THE GEORGE LUCAS TALK SHOW continues to deliver quality livestream content from the digital safety of PLANET SCUM. Retired filmmaker GEORGE LUCAS (notoriously dead-eyed character actor CONNOR RATLIFF) and his CGI co-host WATTO (GRIFFIN NEWMAN, co-lead on Amazon's THE TICK) welcome guests and have fun while actively NOT spreading a disease. They are joined, as always, by snack-snacking, nap-napping producer PATRICK COTNOIR (aka Nickname Jokenoir) whose druthers include you following him on twitter RIGHT NOW: @patrickcotnoir Only a few weeks left in 2020. Who knows what fresh hell awaits us in 2021? Dot dot dot dot..</p>1
 
1.8%
<p>Ghost Dimension's Bex and Sean investigate Chillingham Castle for a Halloween investigation. The most haunted castle in the world. Reports of paranormal activity are daily here, so what will the team manage to capture tonight? as they prepare to spend the night with the ghosts.</p>1
 
1.8%
<p>The crew escaped, barely to the Wistful Wish, while their squiggly pursuers decide to fight instead of flee, and well…see how it worked out for them. Incidents on board also let us see snippets of Ilay &amp; Cycla-919's pasts.</p>1
 
1.8%
<p>Truths come out and accusations fly when members of the TikTok creator mansion turn against one of their own, proving it's every person for themselves.</p>1
 
1.8%
<p>IDOLiSH7 is accused of appropriating Zero's songs, so Takanashi Production holds a press conference. Nagi volunteers to face the press and explains that he was officially given the rights to the song by composer Sakura Haruki. However, a suspicious man dressed as Zero appears at the press conference, aggravating the scandal.</p>1
 
1.8%
<p>On a journey to find love, Chance invites 15 beautiful "ladies" into his home. Each week he will put the hopefuls through various challenges to test their compatibility among other things. However, with constant infighting between the contestants, will Chance be able to finally find his happily ever after?</p>1
 
1.8%

Length

2022-05-09T21:03:26.318016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:03:26.517881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan47
 
10.7%
the21
 
4.8%
to14
 
3.2%
a12
 
2.7%
of8
 
1.8%
and8
 
1.8%
as5
 
1.1%
in4
 
0.9%
his4
 
0.9%
with3
 
0.7%
Other values (276)315
71.4%

Most occurring characters

ValueCountFrequency (%)
385
14.7%
e220
 
8.4%
n220
 
8.4%
a205
 
7.8%
t153
 
5.8%
s138
 
5.3%
o134
 
5.1%
i121
 
4.6%
r101
 
3.9%
h93
 
3.5%
Other values (63)851
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1940
74.0%
Space Separator385
 
14.7%
Uppercase Letter168
 
6.4%
Other Punctuation65
 
2.5%
Math Symbol36
 
1.4%
Decimal Number14
 
0.5%
Dash Punctuation7
 
0.3%
Open Punctuation3
 
0.1%
Close Punctuation3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e220
11.3%
n220
11.3%
a205
10.6%
t153
 
7.9%
s138
 
7.1%
o134
 
6.9%
i121
 
6.2%
r101
 
5.2%
h93
 
4.8%
l79
 
4.1%
Other values (17)476
24.5%
Uppercase Letter
ValueCountFrequency (%)
T19
 
11.3%
C13
 
7.7%
O13
 
7.7%
N12
 
7.1%
E11
 
6.5%
I11
 
6.5%
A11
 
6.5%
R11
 
6.5%
H9
 
5.4%
W8
 
4.8%
Other values (13)50
29.8%
Other Punctuation
ValueCountFrequency (%)
,20
30.8%
.20
30.8%
/9
13.8%
'6
 
9.2%
?3
 
4.6%
"2
 
3.1%
:1
 
1.5%
1
 
1.5%
&1
 
1.5%
;1
 
1.5%
Decimal Number
ValueCountFrequency (%)
24
28.6%
03
21.4%
13
21.4%
92
14.3%
71
 
7.1%
51
 
7.1%
Math Symbol
ValueCountFrequency (%)
>18
50.0%
<18
50.0%
Space Separator
ValueCountFrequency (%)
385
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
80.4%
Common513
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e220
 
10.4%
n220
 
10.4%
a205
 
9.7%
t153
 
7.3%
s138
 
6.5%
o134
 
6.4%
i121
 
5.7%
r101
 
4.8%
h93
 
4.4%
l79
 
3.7%
Other values (40)644
30.6%
Common
ValueCountFrequency (%)
385
75.0%
,20
 
3.9%
.20
 
3.9%
>18
 
3.5%
<18
 
3.5%
/9
 
1.8%
-7
 
1.4%
'6
 
1.2%
24
 
0.8%
03
 
0.6%
Other values (13)23
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2619
99.9%
Punctuation1
 
< 0.1%
None1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
385
14.7%
e220
 
8.4%
n220
 
8.4%
a205
 
7.8%
t153
 
5.8%
s138
 
5.3%
o134
 
5.1%
i121
 
4.6%
r101
 
3.9%
h93
 
3.6%
Other values (61)849
32.4%
Punctuation
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
ä1
100.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43886.19643
Minimum2266
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-05-09T21:03:26.921990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2266
5-th percentile21857.75
Q133039.75
median49844
Q352370
95-th percentile58616.5
Maximum61755
Range59489
Interquartile range (IQR)19330.25

Descriptive statistics

Standard deviation12959.54443
Coefficient of variation (CV)0.2952988749
Kurtosis1.24527437
Mean43886.19643
Median Absolute Deviation (MAD)6389.5
Skewness-1.177620223
Sum2457627
Variance167949791.8
MonotonicityNot monotonic
2022-05-09T21:03:27.086450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
317708
 
14.3%
523033
 
5.4%
349402
 
3.6%
525712
 
3.6%
485971
 
1.8%
583561
 
1.8%
527371
 
1.8%
528581
 
1.8%
533381
 
1.8%
538901
 
1.8%
Other values (35)35
62.5%
ValueCountFrequency (%)
22661
 
1.8%
75911
 
1.8%
187521
 
1.8%
228931
 
1.8%
249631
 
1.8%
306061
 
1.8%
317708
14.3%
334631
 
1.8%
349402
 
3.6%
369071
 
1.8%
ValueCountFrequency (%)
617551
1.8%
599511
1.8%
593981
1.8%
583561
1.8%
578741
1.8%
559861
1.8%
547891
1.8%
540331
1.8%
538901
1.8%
533381
1.8%

_embedded_show_url
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size576.0 B
https://www.tvmaze.com/shows/31770/meisje-van-plezier
https://www.tvmaze.com/shows/52303/pappas-pojkar
 
3
https://www.tvmaze.com/shows/34940/fancy-nancy
 
2
https://www.tvmaze.com/shows/52571/lassemajas-detektivbyra
 
2
https://www.tvmaze.com/shows/48597/manaki
 
1
Other values (40)
40 

Length

Max length77
Median length60
Mean length50.875
Min length40

Characters and Unicode

Total characters2849
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)73.2%

Sample

1st rowhttps://www.tvmaze.com/shows/48597/manaki
2nd rowhttps://www.tvmaze.com/shows/51471/hero-return
3rd rowhttps://www.tvmaze.com/shows/52178/swallowed-star
4th rowhttps://www.tvmaze.com/shows/54033/wu-shen-zhu-zai
5th rowhttps://www.tvmaze.com/shows/49740/tokyo-joshi-pro-wrestling

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/31770/meisje-van-plezier8
 
14.3%
https://www.tvmaze.com/shows/52303/pappas-pojkar3
 
5.4%
https://www.tvmaze.com/shows/34940/fancy-nancy2
 
3.6%
https://www.tvmaze.com/shows/52571/lassemajas-detektivbyra2
 
3.6%
https://www.tvmaze.com/shows/48597/manaki1
 
1.8%
https://www.tvmaze.com/shows/58356/into-the-mother-lands1
 
1.8%
https://www.tvmaze.com/shows/52737/the-george-lucas-talk-show1
 
1.8%
https://www.tvmaze.com/shows/52858/laikykites-ten1
 
1.8%
https://www.tvmaze.com/shows/53338/el-anesa-farah1
 
1.8%
https://www.tvmaze.com/shows/53890/ghost-dimension-lock-down1
 
1.8%
Other values (35)35
62.5%

Length

2022-05-09T21:03:27.248100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/31770/meisje-van-plezier8
 
14.3%
https://www.tvmaze.com/shows/52303/pappas-pojkar3
 
5.4%
https://www.tvmaze.com/shows/34940/fancy-nancy2
 
3.6%
https://www.tvmaze.com/shows/52571/lassemajas-detektivbyra2
 
3.6%
https://www.tvmaze.com/shows/51812/norge-i-krise1
 
1.8%
https://www.tvmaze.com/shows/52178/swallowed-star1
 
1.8%
https://www.tvmaze.com/shows/54033/wu-shen-zhu-zai1
 
1.8%
https://www.tvmaze.com/shows/49740/tokyo-joshi-pro-wrestling1
 
1.8%
https://www.tvmaze.com/shows/50398/mans-diary1
 
1.8%
https://www.tvmaze.com/shows/44659/atlantic-crossing1
 
1.8%
Other values (35)35
62.5%

Most occurring characters

ValueCountFrequency (%)
/280
 
9.8%
w240
 
8.4%
s223
 
7.8%
t208
 
7.3%
o162
 
5.7%
e151
 
5.3%
m139
 
4.9%
a135
 
4.7%
h131
 
4.6%
.112
 
3.9%
Other values (29)1068
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2024
71.0%
Other Punctuation448
 
15.7%
Decimal Number284
 
10.0%
Dash Punctuation93
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w240
11.9%
s223
11.0%
t208
10.3%
o162
 
8.0%
e151
 
7.5%
m139
 
6.9%
a135
 
6.7%
h131
 
6.5%
p90
 
4.4%
c77
 
3.8%
Other values (15)468
23.1%
Decimal Number
ValueCountFrequency (%)
341
14.4%
541
14.4%
735
12.3%
431
10.9%
127
9.5%
927
9.5%
225
8.8%
024
8.5%
820
7.0%
613
 
4.6%
Other Punctuation
ValueCountFrequency (%)
/280
62.5%
.112
 
25.0%
:56
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2024
71.0%
Common825
29.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w240
11.9%
s223
11.0%
t208
10.3%
o162
 
8.0%
e151
 
7.5%
m139
 
6.9%
a135
 
6.7%
h131
 
6.5%
p90
 
4.4%
c77
 
3.8%
Other values (15)468
23.1%
Common
ValueCountFrequency (%)
/280
33.9%
.112
 
13.6%
-93
 
11.3%
:56
 
6.8%
341
 
5.0%
541
 
5.0%
735
 
4.2%
431
 
3.8%
127
 
3.3%
927
 
3.3%
Other values (4)82
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/280
 
9.8%
w240
 
8.4%
s223
 
7.8%
t208
 
7.3%
o162
 
5.7%
e151
 
5.3%
m139
 
4.9%
a135
 
4.7%
h131
 
4.6%
.112
 
3.9%
Other values (29)1068
37.5%

_embedded_show_name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size576.0 B
Meisje van Plezier
Pappas pojkar
 
3
Fancy Nancy
 
2
LasseMajas Detektivbyrå
 
2
Маньяки
 
1
Other values (40)
40 

Length

Max length43
Median length23
Mean length16.19642857
Min length5

Characters and Unicode

Total characters907
Distinct characters91
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)73.2%

Sample

1st rowМаньяки
2nd rowHero Return
3rd rowSwallowed Star
4th rowWu Shen Zhu Zai
5th rowTokyo Joshi Pro Wrestling

Common Values

ValueCountFrequency (%)
Meisje van Plezier8
 
14.3%
Pappas pojkar3
 
5.4%
Fancy Nancy2
 
3.6%
LasseMajas Detektivbyrå2
 
3.6%
Маньяки1
 
1.8%
Into the Mother Lands1
 
1.8%
The George Lucas Talk Show1
 
1.8%
Laikykitės Ten1
 
1.8%
El Anesa Farah1
 
1.8%
Ghost Dimension Lock Down1
 
1.8%
Other values (35)35
62.5%

Length

2022-05-09T21:03:27.372166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
meisje8
 
5.4%
plezier8
 
5.4%
van8
 
5.4%
pojkar3
 
2.0%
the3
 
2.0%
pappas3
 
2.0%
nancy2
 
1.3%
wrestling2
 
1.3%
wwe2
 
1.3%
fancy2
 
1.3%
Other values (104)108
72.5%

Most occurring characters

ValueCountFrequency (%)
93
 
10.3%
e89
 
9.8%
a60
 
6.6%
i55
 
6.1%
n53
 
5.8%
r45
 
5.0%
s44
 
4.9%
o41
 
4.5%
t29
 
3.2%
l27
 
3.0%
Other values (81)371
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter654
72.1%
Uppercase Letter140
 
15.4%
Space Separator93
 
10.3%
Other Punctuation9
 
1.0%
Decimal Number6
 
0.7%
Close Punctuation2
 
0.2%
Currency Symbol2
 
0.2%
Open Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e89
13.6%
a60
 
9.2%
i55
 
8.4%
n53
 
8.1%
r45
 
6.9%
s44
 
6.7%
o41
 
6.3%
t29
 
4.4%
l27
 
4.1%
p18
 
2.8%
Other values (39)193
29.5%
Uppercase Letter
ValueCountFrequency (%)
M16
 
11.4%
P15
 
10.7%
T11
 
7.9%
W9
 
6.4%
S9
 
6.4%
D8
 
5.7%
A8
 
5.7%
L8
 
5.7%
F7
 
5.0%
N6
 
4.3%
Other values (17)43
30.7%
Other Punctuation
ValueCountFrequency (%)
'5
55.6%
#1
 
11.1%
@1
 
11.1%
?1
 
11.1%
:1
 
11.1%
Decimal Number
ValueCountFrequency (%)
02
33.3%
41
16.7%
21
16.7%
31
16.7%
71
16.7%
Currency Symbol
ValueCountFrequency (%)
1
50.0%
$1
50.0%
Space Separator
ValueCountFrequency (%)
93
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin755
83.2%
Common113
 
12.5%
Cyrillic39
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e89
 
11.8%
a60
 
7.9%
i55
 
7.3%
n53
 
7.0%
r45
 
6.0%
s44
 
5.8%
o41
 
5.4%
t29
 
3.8%
l27
 
3.6%
p18
 
2.4%
Other values (43)294
38.9%
Cyrillic
ValueCountFrequency (%)
и4
 
10.3%
а4
 
10.3%
к3
 
7.7%
м3
 
7.7%
о2
 
5.1%
е2
 
5.1%
у2
 
5.1%
я2
 
5.1%
М2
 
5.1%
ь2
 
5.1%
Other values (13)13
33.3%
Common
ValueCountFrequency (%)
93
82.3%
'5
 
4.4%
02
 
1.8%
)2
 
1.8%
41
 
0.9%
#1
 
0.9%
@1
 
0.9%
1
 
0.9%
?1
 
0.9%
$1
 
0.9%
Other values (5)5
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII859
94.7%
Cyrillic39
 
4.3%
None8
 
0.9%
Currency Symbols1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
93
 
10.8%
e89
 
10.4%
a60
 
7.0%
i55
 
6.4%
n53
 
6.2%
r45
 
5.2%
s44
 
5.1%
o41
 
4.8%
t29
 
3.4%
l27
 
3.1%
Other values (52)323
37.6%
Cyrillic
ValueCountFrequency (%)
и4
 
10.3%
а4
 
10.3%
к3
 
7.7%
м3
 
7.7%
о2
 
5.1%
е2
 
5.1%
у2
 
5.1%
я2
 
5.1%
М2
 
5.1%
ь2
 
5.1%
Other values (13)13
33.3%
None
ValueCountFrequency (%)
å3
37.5%
á2
25.0%
ö1
 
12.5%
ø1
 
12.5%
ė1
 
12.5%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size576.0 B
Scripted
26 
Documentary
Animation
Sports
Talk Show
Other values (3)

Length

Max length11
Median length9
Mean length8.232142857
Min length4

Characters and Unicode

Total characters461
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st rowDocumentary
2nd rowAnimation
3rd rowAnimation
4th rowAnimation
5th rowSports

Common Values

ValueCountFrequency (%)
Scripted26
46.4%
Documentary7
 
12.5%
Animation7
 
12.5%
Sports5
 
8.9%
Talk Show5
 
8.9%
Reality3
 
5.4%
News2
 
3.6%
Game Show1
 
1.8%

Length

2022-05-09T21:03:27.520535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:03:27.652310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted26
41.9%
documentary7
 
11.3%
animation7
 
11.3%
show6
 
9.7%
sports5
 
8.1%
talk5
 
8.1%
reality3
 
4.8%
news2
 
3.2%
game1
 
1.6%

Most occurring characters

ValueCountFrequency (%)
t48
10.4%
i43
 
9.3%
e39
 
8.5%
r38
 
8.2%
S37
 
8.0%
c33
 
7.2%
p31
 
6.7%
d26
 
5.6%
o25
 
5.4%
a23
 
5.0%
Other values (16)118
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter393
85.2%
Uppercase Letter62
 
13.4%
Space Separator6
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t48
12.2%
i43
10.9%
e39
9.9%
r38
9.7%
c33
8.4%
p31
7.9%
d26
 
6.6%
o25
 
6.4%
a23
 
5.9%
n21
 
5.3%
Other values (8)66
16.8%
Uppercase Letter
ValueCountFrequency (%)
S37
59.7%
D7
 
11.3%
A7
 
11.3%
T5
 
8.1%
R3
 
4.8%
N2
 
3.2%
G1
 
1.6%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin455
98.7%
Common6
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t48
10.5%
i43
 
9.5%
e39
 
8.6%
r38
 
8.4%
S37
 
8.1%
c33
 
7.3%
p31
 
6.8%
d26
 
5.7%
o25
 
5.5%
a23
 
5.1%
Other values (15)112
24.6%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII461
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t48
10.4%
i43
 
9.3%
e39
 
8.5%
r38
 
8.2%
S37
 
8.0%
c33
 
7.2%
p31
 
6.7%
d26
 
5.6%
o25
 
5.4%
a23
 
5.0%
Other values (16)118
25.6%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size576.0 B
English
14 
Dutch
Swedish
Norwegian
Russian
Other values (11)
19 

Length

Max length10
Median length7
Mean length7.053571429
Min length5

Characters and Unicode

Total characters395
Distinct characters32
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)12.5%

Sample

1st rowRussian
2nd rowChinese
3rd rowChinese
4th rowChinese
5th rowJapanese

Common Values

ValueCountFrequency (%)
English14
25.0%
Dutch8
14.3%
Swedish6
10.7%
Norwegian5
 
8.9%
Russian4
 
7.1%
Chinese4
 
7.1%
Japanese4
 
7.1%
Spanish2
 
3.6%
Arabic2
 
3.6%
Korean1
 
1.8%
Other values (6)6
10.7%

Length

2022-05-09T21:03:27.802240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english14
25.0%
dutch8
14.3%
swedish6
10.7%
norwegian5
 
8.9%
russian4
 
7.1%
chinese4
 
7.1%
japanese4
 
7.1%
spanish2
 
3.6%
arabic2
 
3.6%
korean1
 
1.8%
Other values (6)6
10.7%

Most occurring characters

ValueCountFrequency (%)
i43
 
10.9%
n41
 
10.4%
s40
 
10.1%
h36
 
9.1%
a31
 
7.8%
e30
 
7.6%
g22
 
5.6%
l15
 
3.8%
u15
 
3.8%
E14
 
3.5%
Other values (22)108
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter339
85.8%
Uppercase Letter56
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i43
12.7%
n41
12.1%
s40
11.8%
h36
10.6%
a31
9.1%
e30
8.8%
g22
 
6.5%
l15
 
4.4%
u15
 
4.4%
w11
 
3.2%
Other values (9)55
16.2%
Uppercase Letter
ValueCountFrequency (%)
E14
25.0%
D9
16.1%
S8
14.3%
N5
 
8.9%
R5
 
8.9%
C4
 
7.1%
J4
 
7.1%
A2
 
3.6%
K1
 
1.8%
U1
 
1.8%
Other values (3)3
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin395
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i43
 
10.9%
n41
 
10.4%
s40
 
10.1%
h36
 
9.1%
a31
 
7.8%
e30
 
7.6%
g22
 
5.6%
l15
 
3.8%
u15
 
3.8%
E14
 
3.5%
Other values (22)108
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i43
 
10.9%
n41
 
10.4%
s40
 
10.1%
h36
 
9.1%
a31
 
7.8%
e30
 
7.6%
g22
 
5.6%
l15
 
3.8%
u15
 
3.8%
E14
 
3.5%
Other values (22)108
27.3%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Memory size576.0 B
[]
16 
['Drama', 'Adult']
['Comedy']
['Comedy', 'Adventure', 'Children']
 
2
['Sports']
 
2
Other values (20)
21 

Length

Max length43
Median length35
Mean length15.67857143
Min length2

Characters and Unicode

Total characters878
Distinct characters33
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)33.9%

Sample

1st row[]
2nd row['Action', 'Anime', 'Science-Fiction']
3rd row['Anime', 'Science-Fiction']
4th row['Action', 'Adventure', 'Anime', 'Fantasy']
5th row[]

Common Values

ValueCountFrequency (%)
[]16
28.6%
['Drama', 'Adult']8
14.3%
['Comedy']7
12.5%
['Comedy', 'Adventure', 'Children']2
 
3.6%
['Sports']2
 
3.6%
['Drama', 'Family', 'Mystery']2
 
3.6%
['Drama', 'Children', 'Family']1
 
1.8%
['Comedy', 'Romance']1
 
1.8%
['Anime', 'Science-Fiction']1
 
1.8%
['Anime', 'Music']1
 
1.8%
Other values (15)15
26.8%

Length

2022-05-09T21:03:27.918600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama17
17.3%
16
16.3%
comedy14
14.3%
adult8
 
8.2%
anime5
 
5.1%
children4
 
4.1%
family4
 
4.1%
mystery4
 
4.1%
sports3
 
3.1%
romance3
 
3.1%
Other values (11)20
20.4%

Most occurring characters

ValueCountFrequency (%)
'164
18.7%
[56
 
6.4%
]56
 
6.4%
a46
 
5.2%
e46
 
5.2%
r46
 
5.2%
m45
 
5.1%
,42
 
4.8%
42
 
4.8%
o32
 
3.6%
Other values (23)303
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter430
49.0%
Other Punctuation206
23.5%
Uppercase Letter85
 
9.7%
Open Punctuation56
 
6.4%
Close Punctuation56
 
6.4%
Space Separator42
 
4.8%
Dash Punctuation3
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a46
10.7%
e46
10.7%
r46
10.7%
m45
10.5%
o32
7.4%
i31
7.2%
d29
6.7%
y29
6.7%
t28
 
6.5%
n26
 
6.0%
Other values (7)72
16.7%
Uppercase Letter
ValueCountFrequency (%)
C20
23.5%
A19
22.4%
D17
20.0%
F8
 
9.4%
S7
 
8.2%
M5
 
5.9%
H4
 
4.7%
R3
 
3.5%
T1
 
1.2%
W1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
'164
79.6%
,42
 
20.4%
Open Punctuation
ValueCountFrequency (%)
[56
100.0%
Close Punctuation
ValueCountFrequency (%)
]56
100.0%
Space Separator
ValueCountFrequency (%)
42
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin515
58.7%
Common363
41.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a46
 
8.9%
e46
 
8.9%
r46
 
8.9%
m45
 
8.7%
o32
 
6.2%
i31
 
6.0%
d29
 
5.6%
y29
 
5.6%
t28
 
5.4%
n26
 
5.0%
Other values (17)157
30.5%
Common
ValueCountFrequency (%)
'164
45.2%
[56
 
15.4%
]56
 
15.4%
,42
 
11.6%
42
 
11.6%
-3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII878
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'164
18.7%
[56
 
6.4%
]56
 
6.4%
a46
 
5.2%
e46
 
5.2%
r46
 
5.2%
m45
 
5.1%
,42
 
4.8%
42
 
4.8%
o32
 
3.6%
Other values (23)303
34.5%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size576.0 B
Running
44 
Ended
To Be Determined

Length

Max length16
Median length7
Mean length7.553571429
Min length5

Characters and Unicode

Total characters423
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowRunning
3rd rowRunning
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Running44
78.6%
Ended7
 
12.5%
To Be Determined5
 
8.9%

Length

2022-05-09T21:03:28.013882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:03:28.107866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
running44
66.7%
ended7
 
10.6%
to5
 
7.6%
be5
 
7.6%
determined5
 
7.6%

Most occurring characters

ValueCountFrequency (%)
n144
34.0%
i49
 
11.6%
R44
 
10.4%
u44
 
10.4%
g44
 
10.4%
e27
 
6.4%
d19
 
4.5%
10
 
2.4%
E7
 
1.7%
T5
 
1.2%
Other values (6)30
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter347
82.0%
Uppercase Letter66
 
15.6%
Space Separator10
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n144
41.5%
i49
 
14.1%
u44
 
12.7%
g44
 
12.7%
e27
 
7.8%
d19
 
5.5%
o5
 
1.4%
t5
 
1.4%
r5
 
1.4%
m5
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
R44
66.7%
E7
 
10.6%
T5
 
7.6%
B5
 
7.6%
D5
 
7.6%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin413
97.6%
Common10
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n144
34.9%
i49
 
11.9%
R44
 
10.7%
u44
 
10.7%
g44
 
10.7%
e27
 
6.5%
d19
 
4.6%
E7
 
1.7%
T5
 
1.2%
o5
 
1.2%
Other values (5)25
 
6.1%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII423
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n144
34.0%
i49
 
11.6%
R44
 
10.4%
u44
 
10.4%
g44
 
10.4%
e27
 
6.4%
d19
 
4.5%
10
 
2.4%
E7
 
1.7%
T5
 
1.2%
Other values (6)30
 
7.1%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct14
Distinct (%)31.8%
Missing12
Missing (%)21.4%
Infinite0
Infinite (%)0.0%
Mean48.11363636
Minimum8
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-05-09T21:03:28.209886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile12.45
Q122
median43.5
Q346.25
95-th percentile120
Maximum180
Range172
Interquartile range (IQR)24.25

Descriptive statistics

Standard deviation36.71082212
Coefficient of variation (CV)0.7630024437
Kurtosis3.400343047
Mean48.11363636
Median Absolute Deviation (MAD)16.5
Skewness1.837736289
Sum2117
Variance1347.684461
MonotonicityNot monotonic
2022-05-09T21:03:28.314514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4511
19.6%
1205
8.9%
604
 
7.1%
304
 
7.1%
403
 
5.4%
153
 
5.4%
203
 
5.4%
223
 
5.4%
122
 
3.6%
422
 
3.6%
Other values (4)4
 
7.1%
(Missing)12
21.4%
ValueCountFrequency (%)
81
 
1.8%
122
 
3.6%
153
 
5.4%
203
 
5.4%
223
 
5.4%
251
 
1.8%
304
 
7.1%
403
 
5.4%
422
 
3.6%
4511
19.6%
ValueCountFrequency (%)
1801
 
1.8%
1205
8.9%
604
 
7.1%
501
 
1.8%
4511
19.6%
422
 
3.6%
403
 
5.4%
304
 
7.1%
251
 
1.8%
223
 
5.4%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)53.8%
Missing4
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean46.84615385
Minimum5
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-05-09T21:03:28.422225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.65
Q122
median42
Q355.5
95-th percentile120
Maximum188
Range183
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation34.95003976
Coefficient of variation (CV)0.746059962
Kurtosis4.729427638
Mean46.84615385
Median Absolute Deviation (MAD)16
Skewness1.925749485
Sum2436
Variance1221.505279
MonotonicityNot monotonic
2022-05-09T21:03:28.555486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
488
 
14.3%
1204
 
7.1%
223
 
5.4%
293
 
5.4%
402
 
3.6%
152
 
3.6%
572
 
3.6%
452
 
3.6%
302
 
3.6%
602
 
3.6%
Other values (18)22
39.3%
(Missing)4
 
7.1%
ValueCountFrequency (%)
51
 
1.8%
81
 
1.8%
91
 
1.8%
122
3.6%
152
3.6%
161
 
1.8%
202
3.6%
211
 
1.8%
223
5.4%
251
 
1.8%
ValueCountFrequency (%)
1881
 
1.8%
1204
7.1%
971
 
1.8%
751
 
1.8%
641
 
1.8%
602
 
3.6%
591
 
1.8%
572
 
3.6%
551
 
1.8%
488
14.3%

_embedded_show_premiered
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Memory size576.0 B
2017-09-10
2020-12-06
2020-11-29
2020-11-22
 
3
2018-07-13
 
2
Other values (34)
34 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters560
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)60.7%

Sample

1st row2020-05-22
2nd row2020-10-18
3rd row2020-11-29
4th row2020-03-08
5th row2013-01-30

Common Values

ValueCountFrequency (%)
2017-09-108
 
14.3%
2020-12-065
 
8.9%
2020-11-294
 
7.1%
2020-11-223
 
5.4%
2018-07-132
 
3.6%
2019-07-291
 
1.8%
2018-10-111
 
1.8%
2020-05-041
 
1.8%
2016-09-111
 
1.8%
2019-12-291
 
1.8%
Other values (29)29
51.8%

Length

2022-05-09T21:03:28.677637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-09-108
 
14.3%
2020-12-065
 
8.9%
2020-11-294
 
7.1%
2020-11-223
 
5.4%
2018-07-132
 
3.6%
2020-10-131
 
1.8%
2020-03-081
 
1.8%
2013-01-301
 
1.8%
2019-07-211
 
1.8%
2020-10-251
 
1.8%
Other values (29)29
51.8%

Most occurring characters

ValueCountFrequency (%)
0149
26.6%
2118
21.1%
-112
20.0%
198
17.5%
923
 
4.1%
716
 
2.9%
612
 
2.1%
810
 
1.8%
39
 
1.6%
58
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number448
80.0%
Dash Punctuation112
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0149
33.3%
2118
26.3%
198
21.9%
923
 
5.1%
716
 
3.6%
612
 
2.7%
810
 
2.2%
39
 
2.0%
58
 
1.8%
45
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
-112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common560
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0149
26.6%
2118
21.1%
-112
20.0%
198
17.5%
923
 
4.1%
716
 
2.9%
612
 
2.1%
810
 
1.8%
39
 
1.6%
58
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0149
26.6%
2118
21.1%
-112
20.0%
198
17.5%
923
 
4.1%
716
 
2.9%
612
 
2.1%
810
 
1.8%
39
 
1.6%
58
 
1.4%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size576.0 B
nan
49 
2020-12-13
 
2
2020-12-24
 
1
2020-12-27
 
1
2021-03-13
 
1
Other values (2)
 
2

Length

Max length10
Median length3
Mean length3.875
Min length3

Characters and Unicode

Total characters217
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)8.9%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan49
87.5%
2020-12-132
 
3.6%
2020-12-241
 
1.8%
2020-12-271
 
1.8%
2021-03-131
 
1.8%
2021-01-311
 
1.8%
2021-11-281
 
1.8%

Length

2022-05-09T21:03:28.780976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:03:28.928731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan49
87.5%
2020-12-132
 
3.6%
2020-12-241
 
1.8%
2020-12-271
 
1.8%
2021-03-131
 
1.8%
2021-01-311
 
1.8%
2021-11-281
 
1.8%

Most occurring characters

ValueCountFrequency (%)
n98
45.2%
a49
22.6%
221
 
9.7%
-14
 
6.5%
114
 
6.5%
013
 
6.0%
35
 
2.3%
41
 
0.5%
71
 
0.5%
81
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter147
67.7%
Decimal Number56
 
25.8%
Dash Punctuation14
 
6.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
221
37.5%
114
25.0%
013
23.2%
35
 
8.9%
41
 
1.8%
71
 
1.8%
81
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
n98
66.7%
a49
33.3%
Dash Punctuation
ValueCountFrequency (%)
-14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin147
67.7%
Common70
32.3%

Most frequent character per script

Common
ValueCountFrequency (%)
221
30.0%
-14
20.0%
114
20.0%
013
18.6%
35
 
7.1%
41
 
1.4%
71
 
1.4%
81
 
1.4%
Latin
ValueCountFrequency (%)
n98
66.7%
a49
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n98
45.2%
a49
22.6%
221
 
9.7%
-14
 
6.5%
114
 
6.5%
013
 
6.0%
35
 
2.3%
41
 
0.5%
71
 
0.5%
81
 
0.5%

_embedded_show_officialSite
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size576.0 B
https://www.videoland.com/series/1062/meisje-van-plezier/1976
https://www.discoveryplus.se/program/pappas-pojkar
 
3
https://disneynow.com/shows/fancy-nancy
 
2
https://www.cmore.se/serie/208411-lassemajas-detektivbyra
 
2
https://premier.one/show/12420
 
1
Other values (40)
40 

Length

Max length85
Median length59
Mean length48.64285714
Min length3

Characters and Unicode

Total characters2724
Distinct characters68
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)73.2%

Sample

1st rowhttps://premier.one/show/12420
2nd rowhttps://v.qq.com/detail/q/q72jd29a3oxflsr.html
3rd rowhttps://v.qq.com/detail/3/324olz7ilvo2j5f.html
4th rowhttps://v.qq.com/detail/m/7q544xyrava3vxf.html
5th rowhttps://www.ddtpro.com/

Common Values

ValueCountFrequency (%)
https://www.videoland.com/series/1062/meisje-van-plezier/19768
 
14.3%
https://www.discoveryplus.se/program/pappas-pojkar3
 
5.4%
https://disneynow.com/shows/fancy-nancy2
 
3.6%
https://www.cmore.se/serie/208411-lassemajas-detektivbyra2
 
3.6%
https://premier.one/show/124201
 
1.8%
https://motherlandsrpg.com1
 
1.8%
https://www.patrickcotnoir.com/glts1
 
1.8%
http://www.laisves.tv1
 
1.8%
https://shahid.mbc.net/en/series/Al-Anisa-Farah/series-3936341
 
1.8%
https://www.amazon.co.uk/Ghost-Dimension-Lock-Down/dp/B08NVYQ73R1
 
1.8%
Other values (35)35
62.5%

Length

2022-05-09T21:03:29.171363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.videoland.com/series/1062/meisje-van-plezier/19768
 
14.3%
https://www.discoveryplus.se/program/pappas-pojkar3
 
5.4%
https://disneynow.com/shows/fancy-nancy2
 
3.6%
https://www.cmore.se/serie/208411-lassemajas-detektivbyra2
 
3.6%
https://sumo.tv2.no/programmer/fakta/norge-i-krise1
 
1.8%
https://v.qq.com/detail/3/324olz7ilvo2j5f.html1
 
1.8%
https://v.qq.com/detail/m/7q544xyrava3vxf.html1
 
1.8%
https://www.ddtpro.com1
 
1.8%
https://www.bilibili.com/bangumi/media/md43146221
 
1.8%
https://tv.nrk.no/serie/atlantic-crossing1
 
1.8%
Other values (35)35
62.5%

Most occurring characters

ValueCountFrequency (%)
/248
 
9.1%
e199
 
7.3%
t182
 
6.7%
s173
 
6.4%
o128
 
4.7%
a119
 
4.4%
i115
 
4.2%
w114
 
4.2%
p108
 
4.0%
.104
 
3.8%
Other values (58)1234
45.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1954
71.7%
Other Punctuation410
 
15.1%
Decimal Number204
 
7.5%
Dash Punctuation77
 
2.8%
Uppercase Letter75
 
2.8%
Math Symbol2
 
0.1%
Connector Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e199
 
10.2%
t182
 
9.3%
s173
 
8.9%
o128
 
6.6%
a119
 
6.1%
i115
 
5.9%
w114
 
5.8%
p108
 
5.5%
n98
 
5.0%
r95
 
4.9%
Other values (16)623
31.9%
Uppercase Letter
ValueCountFrequency (%)
L6
 
8.0%
C6
 
8.0%
B6
 
8.0%
H5
 
6.7%
F5
 
6.7%
A5
 
6.7%
T5
 
6.7%
D4
 
5.3%
Y4
 
5.3%
W3
 
4.0%
Other values (14)26
34.7%
Decimal Number
ValueCountFrequency (%)
133
16.2%
230
14.7%
626
12.7%
722
10.8%
022
10.8%
419
9.3%
318
8.8%
917
8.3%
59
 
4.4%
88
 
3.9%
Other Punctuation
ValueCountFrequency (%)
/248
60.5%
.104
25.4%
:55
 
13.4%
?2
 
0.5%
%1
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
-77
100.0%
Math Symbol
ValueCountFrequency (%)
=2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2029
74.5%
Common695
 
25.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e199
 
9.8%
t182
 
9.0%
s173
 
8.5%
o128
 
6.3%
a119
 
5.9%
i115
 
5.7%
w114
 
5.6%
p108
 
5.3%
n98
 
4.8%
r95
 
4.7%
Other values (40)698
34.4%
Common
ValueCountFrequency (%)
/248
35.7%
.104
15.0%
-77
 
11.1%
:55
 
7.9%
133
 
4.7%
230
 
4.3%
626
 
3.7%
722
 
3.2%
022
 
3.2%
419
 
2.7%
Other values (8)59
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/248
 
9.1%
e199
 
7.3%
t182
 
6.7%
s173
 
6.4%
o128
 
4.7%
a119
 
4.4%
i115
 
4.2%
w114
 
4.2%
p108
 
4.0%
.104
 
3.8%
Other values (58)1234
45.3%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.55357143
Minimum2
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-05-09T21:03:29.290722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.75
Q115
median36.5
Q373.25
95-th percentile84.25
Maximum88
Range86
Interquartile range (IQR)58.25

Descriptive statistics

Standard deviation28.57775336
Coefficient of variation (CV)0.7046914082
Kurtosis-1.454324592
Mean40.55357143
Median Absolute Deviation (MAD)25.5
Skewness0.1981907925
Sum2271
Variance816.687987
MonotonicityNot monotonic
2022-05-09T21:03:29.394846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
748
 
14.3%
883
 
5.4%
33
 
5.4%
273
 
5.4%
23
 
5.4%
152
 
3.6%
182
 
3.6%
592
 
3.6%
62
 
3.6%
442
 
3.6%
Other values (22)26
46.4%
ValueCountFrequency (%)
23
5.4%
33
5.4%
62
3.6%
81
 
1.8%
102
3.6%
121
 
1.8%
131
 
1.8%
152
3.6%
171
 
1.8%
182
3.6%
ValueCountFrequency (%)
883
 
5.4%
831
 
1.8%
762
 
3.6%
748
14.3%
731
 
1.8%
702
 
3.6%
601
 
1.8%
592
 
3.6%
571
 
1.8%
531
 
1.8%

_embedded_show_dvdCountry
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size576.0 B
nan
55 
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}
 
1

Length

Max length66
Median length3
Mean length4.125
Min length3

Characters and Unicode

Total characters231
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan55
98.2%
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}1
 
1.8%

Length

2022-05-09T21:03:29.496182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:03:29.573957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan55
90.2%
name1
 
1.6%
ukraine1
 
1.6%
code1
 
1.6%
ua1
 
1.6%
timezone1
 
1.6%
europe/zaporozhye1
 
1.6%

Most occurring characters

ValueCountFrequency (%)
n113
48.9%
a58
25.1%
'12
 
5.2%
e7
 
3.0%
o5
 
2.2%
5
 
2.2%
:3
 
1.3%
r3
 
1.3%
i2
 
0.9%
p2
 
0.9%
Other values (17)21
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter201
87.0%
Other Punctuation18
 
7.8%
Space Separator5
 
2.2%
Uppercase Letter5
 
2.2%
Open Punctuation1
 
0.4%
Close Punctuation1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n113
56.2%
a58
28.9%
e7
 
3.5%
o5
 
2.5%
r3
 
1.5%
i2
 
1.0%
p2
 
1.0%
z2
 
1.0%
m2
 
1.0%
u1
 
0.5%
Other values (6)6
 
3.0%
Other Punctuation
ValueCountFrequency (%)
'12
66.7%
:3
 
16.7%
,2
 
11.1%
/1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
U2
40.0%
Z1
20.0%
E1
20.0%
A1
20.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
{1
100.0%
Close Punctuation
ValueCountFrequency (%)
}1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin206
89.2%
Common25
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n113
54.9%
a58
28.2%
e7
 
3.4%
o5
 
2.4%
r3
 
1.5%
i2
 
1.0%
p2
 
1.0%
z2
 
1.0%
U2
 
1.0%
m2
 
1.0%
Other values (10)10
 
4.9%
Common
ValueCountFrequency (%)
'12
48.0%
5
20.0%
:3
 
12.0%
,2
 
8.0%
/1
 
4.0%
{1
 
4.0%
}1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII231
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n113
48.9%
a58
25.1%
'12
 
5.2%
e7
 
3.0%
o5
 
2.2%
5
 
2.2%
:3
 
1.3%
r3
 
1.3%
i2
 
0.9%
p2
 
0.9%
Other values (17)21
 
9.1%

_embedded_show_summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size576.0 B
<p>The marriage of Nadine and Remco seems to be ok: he is making earnings, she is taking care of their kids. Everything seems to be fine, until Remco confesses his cheating on her with her halfsister and leaves. Nadine is inconsolable. But behind her tears, a small fire is spreading. She wants to fight for her self-esteem and radically changes her life.</p>
nan
<p>Once they were the coolest guys in school. Ten years later, they are still partying as if they were carefree teenagers. Now it's high time for daddy's boys to grow up.</p>
 
3
<p><b>Fancy Nancy</b> centers around six-year-old Nancy, a girl who likes to be fancy in everything from her advanced vocabulary to her creative, elaborate attire. Excited to experience what the magnificent world has to offer, Nancy uses her ingenuity and imagination as she learns that while life doesn't always go as planned, it's important to celebrate the differences that make everyone unique.</p>
 
2
<p>This family series revolves around the two friends Lasse and Maja who run a detective agency together in the small town of Valleby. They investigate all possible mysteries and contribute crucial pieces to the police chief, played by Anders Jansson.</p>
 
2
Other values (35)
35 

Length

Max length851
Median length457
Mean length331.9285714
Min length3

Characters and Unicode

Total characters18588
Distinct characters102
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)62.5%

Sample

1st row<p>The non-fiction series "Maniacs" tells about the most high-profile Russian crimes, in which still not all the circumstances are clear to the end</p>
2nd row<p>Zero was mankind's first real superhero. Under his watch, countless other superheros appeared and followed in his footsteps. However, after 5 years of war, Zero disappeared without a trace.<br /><br />(Source: zeroscans)</p>
3rd row<p>One day, an unexplained RR virus appeared on the earth, drawing the world into disaster. Infected animals mutated into terrible monsters, invaded massively, and humans built walls around the destruction and established the base city as the last bastion for humans. The suffering that mankind has experienced during this period of time is known as the "Great Nirvana Period." Not only that, Luo Feng not only carried the burden of supporting the family but also to protect the human homeland, for the better survival and development of mankind, together with other justice warriors, to join hands against the fierce monsters. Under the desperate situation of the end, can Luo Feng and other warriors repel monsters and successfully protect the human world?</p>
4th row<p>The protagonist Qin Chen, who was originally the top genius in the military domain, was conspired by the people to fall into the death canyon in the forbidden land of the mainland. Qin Chen, who was inevitably dead, unexpectedly triggered the power of the mysterious ancient sword.<br /><br />Three hundred years later, in a remote part of the Tianwu mainland, a boy of the same name accidentally inherited Qin Chen's will. As the beloved grandson of King Dingwu of the Daqi National Army, due to the birth father's birth, the mother and son were treated coldly in Dingwu's palace and lived together. In order to rewrite the myth of the strong man in hope of the sun, and to protect everything he loves, Qin Chen resolutely took up the responsibility of maintaining the five kingdoms of the world and set foot on the road of martial arts again.</p>
5th rownan

Common Values

ValueCountFrequency (%)
<p>The marriage of Nadine and Remco seems to be ok: he is making earnings, she is taking care of their kids. Everything seems to be fine, until Remco confesses his cheating on her with her halfsister and leaves. Nadine is inconsolable. But behind her tears, a small fire is spreading. She wants to fight for her self-esteem and radically changes her life.</p>8
 
14.3%
nan6
 
10.7%
<p>Once they were the coolest guys in school. Ten years later, they are still partying as if they were carefree teenagers. Now it's high time for daddy's boys to grow up.</p>3
 
5.4%
<p><b>Fancy Nancy</b> centers around six-year-old Nancy, a girl who likes to be fancy in everything from her advanced vocabulary to her creative, elaborate attire. Excited to experience what the magnificent world has to offer, Nancy uses her ingenuity and imagination as she learns that while life doesn't always go as planned, it's important to celebrate the differences that make everyone unique.</p>2
 
3.6%
<p>This family series revolves around the two friends Lasse and Maja who run a detective agency together in the small town of Valleby. They investigate all possible mysteries and contribute crucial pieces to the police chief, played by Anders Jansson.</p>2
 
3.6%
<p><b>AwesomenessTV's Next Influencer</b> follows a group of content creators competing in a series of challenges to prove they have what it takes to become the next big influencer.</p>1
 
1.8%
<p><b>The George Lucas Talk Show</b>, a long-running cult talk show hosted by Connor Ratliff, as George Lucas, his sidekick Watto (Griffin Newman), and his producer Patrick Cotnoir. They interview guests in a panel format weekly on PlanetScum.</p>1
 
1.8%
<p>A show of intellectual satire. The show discusses national and foreign issues in a witty and biting way, and once a month - a topic prepared in detail by the screenwriters. Since the beginning of the fifth season, three hosts have shared the main wheel: Andrius Tapinas, Ignas Grinevičius, and Irma Bogdanovičiūtė.</p>1
 
1.8%
<p>Farah and Shadi are left to deal with the consequences of their son's kidnapping. Between war and peace, Majed and Dalal rediscover their relationship.</p>1
 
1.8%
<p><b>Ghost Dimension Lock Down</b> paranormal investigators take on the UK's most haunting buildings, searching for paranormal activity.  While the world is on lockdown, it appears that the ghosts are not. </p>1
 
1.8%
Other values (30)30
53.6%

Length

2022-05-09T21:03:29.684481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the172
 
5.6%
and116
 
3.8%
to95
 
3.1%
of89
 
2.9%
a63
 
2.1%
is60
 
2.0%
her56
 
1.8%
in55
 
1.8%
with29
 
1.0%
they26
 
0.9%
Other values (1169)2287
75.0%

Most occurring characters

ValueCountFrequency (%)
2987
16.1%
e1839
 
9.9%
a1175
 
6.3%
t1154
 
6.2%
n1061
 
5.7%
i1042
 
5.6%
s1024
 
5.5%
o993
 
5.3%
r911
 
4.9%
h761
 
4.1%
Other values (92)5641
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14133
76.0%
Space Separator2994
 
16.1%
Uppercase Letter548
 
2.9%
Other Punctuation495
 
2.7%
Math Symbol316
 
1.7%
Dash Punctuation40
 
0.2%
Decimal Number34
 
0.2%
Open Punctuation8
 
< 0.1%
Close Punctuation8
 
< 0.1%
Other Letter8
 
< 0.1%
Other values (3)4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1839
13.0%
a1175
 
8.3%
t1154
 
8.2%
n1061
 
7.5%
i1042
 
7.4%
s1024
 
7.2%
o993
 
7.0%
r911
 
6.4%
h761
 
5.4%
l556
 
3.9%
Other values (24)3617
25.6%
Uppercase Letter
ValueCountFrequency (%)
T61
 
11.1%
S53
 
9.7%
N47
 
8.6%
W46
 
8.4%
R30
 
5.5%
E27
 
4.9%
H22
 
4.0%
A22
 
4.0%
F21
 
3.8%
G21
 
3.8%
Other values (17)198
36.1%
Other Punctuation
ValueCountFrequency (%)
,165
33.3%
.164
33.1%
/82
16.6%
'35
 
7.1%
"15
 
3.0%
:14
 
2.8%
!12
 
2.4%
?6
 
1.2%
@1
 
0.2%
#1
 
0.2%
Decimal Number
ValueCountFrequency (%)
012
35.3%
27
20.6%
15
14.7%
83
 
8.8%
93
 
8.8%
42
 
5.9%
51
 
2.9%
71
 
2.9%
Other Letter
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Dash Punctuation
ValueCountFrequency (%)
-30
75.0%
8
 
20.0%
2
 
5.0%
Space Separator
ValueCountFrequency (%)
2987
99.8%
 7
 
0.2%
Math Symbol
ValueCountFrequency (%)
<158
50.0%
>158
50.0%
Open Punctuation
ValueCountFrequency (%)
(7
87.5%
[1
 
12.5%
Close Punctuation
ValueCountFrequency (%)
)7
87.5%
]1
 
12.5%
Currency Symbol
ValueCountFrequency (%)
$1
50.0%
1
50.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Modifier Letter
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14681
79.0%
Common3899
 
21.0%
Han4
 
< 0.1%
Katakana4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1839
12.5%
a1175
 
8.0%
t1154
 
7.9%
n1061
 
7.2%
i1042
 
7.1%
s1024
 
7.0%
o993
 
6.8%
r911
 
6.2%
h761
 
5.2%
l556
 
3.8%
Other values (51)4165
28.4%
Common
ValueCountFrequency (%)
2987
76.6%
,165
 
4.2%
.164
 
4.2%
<158
 
4.1%
>158
 
4.1%
/82
 
2.1%
'35
 
0.9%
-30
 
0.8%
"15
 
0.4%
:14
 
0.4%
Other values (23)91
 
2.3%
Han
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18544
99.8%
None23
 
0.1%
Punctuation10
 
0.1%
Katakana5
 
< 0.1%
CJK4
 
< 0.1%
Dingbats1
 
< 0.1%
Currency Symbols1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2987
16.1%
e1839
 
9.9%
a1175
 
6.3%
t1154
 
6.2%
n1061
 
5.7%
i1042
 
5.6%
s1024
 
5.5%
o993
 
5.4%
r911
 
4.9%
h761
 
4.1%
Other values (69)5597
30.2%
Punctuation
ValueCountFrequency (%)
8
80.0%
2
 
20.0%
None
ValueCountFrequency (%)
 7
30.4%
ä3
13.0%
Å2
 
8.7%
å2
 
8.7%
é2
 
8.7%
ö2
 
8.7%
č2
 
8.7%
ā1
 
4.3%
ė1
 
4.3%
ū1
 
4.3%
CJK
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Dingbats
ValueCountFrequency (%)
1
100.0%
Katakana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1630359132
Minimum1603467037
Maximum1651764342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-05-09T21:03:29.805746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1603467037
5-th percentile1607867000
Q11611904902
median1629692845
Q31648343911
95-th percentile1651474191
Maximum1651764342
Range48297305
Interquartile range (IQR)36439009

Descriptive statistics

Standard deviation17037980
Coefficient of variation (CV)0.01045044596
Kurtosis-1.580063365
Mean1630359132
Median Absolute Deviation (MAD)18619733.5
Skewness-0.09278889687
Sum9.130011138 × 1010
Variance2.902927624 × 1014
MonotonicityNot monotonic
2022-05-09T21:03:29.937294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
16078670008
 
14.3%
16264292643
 
5.4%
16349480182
 
3.6%
16219269062
 
3.6%
16438990391
 
1.8%
16344496981
 
1.8%
16182435821
 
1.8%
16497746891
 
1.8%
16450396161
 
1.8%
16148543711
 
1.8%
Other values (35)35
62.5%
ValueCountFrequency (%)
16034670371
 
1.8%
16074646181
 
1.8%
16078670008
14.3%
16085040201
 
1.8%
16096167881
 
1.8%
16110394971
 
1.8%
16114368421
 
1.8%
16120609221
 
1.8%
16148543711
 
1.8%
16179867351
 
1.8%
ValueCountFrequency (%)
16517643421
1.8%
16517491651
1.8%
16516456841
1.8%
16514170271
1.8%
16512600631
1.8%
16509836761
1.8%
16509088001
1.8%
16497746891
1.8%
16495725941
1.8%
16494234441
1.8%

_links_self_href
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size576.0 B
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/2015818
 
1
https://api.tvmaze.com/episodes/2169203
 
1
https://api.tvmaze.com/episodes/2312223
 
1
https://api.tvmaze.com/episodes/2312224
 
1
Other values (51)
51 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters2184
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.8%
https://api.tvmaze.com/episodes/20158181
 
1.8%
https://api.tvmaze.com/episodes/21692031
 
1.8%
https://api.tvmaze.com/episodes/23122231
 
1.8%
https://api.tvmaze.com/episodes/23122241
 
1.8%
https://api.tvmaze.com/episodes/23122251
 
1.8%
https://api.tvmaze.com/episodes/23122261
 
1.8%
https://api.tvmaze.com/episodes/23122271
 
1.8%
https://api.tvmaze.com/episodes/23122281
 
1.8%
https://api.tvmaze.com/episodes/20875881
 
1.8%
Other values (46)46
82.1%

Length

2022-05-09T21:03:30.057794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.8%
https://api.tvmaze.com/episodes/20158181
 
1.8%
https://api.tvmaze.com/episodes/23244331
 
1.8%
https://api.tvmaze.com/episodes/19640001
 
1.8%
https://api.tvmaze.com/episodes/19954051
 
1.8%
https://api.tvmaze.com/episodes/20077601
 
1.8%
https://api.tvmaze.com/episodes/19857891
 
1.8%
https://api.tvmaze.com/episodes/20396221
 
1.8%
https://api.tvmaze.com/episodes/20396231
 
1.8%
https://api.tvmaze.com/episodes/23244271
 
1.8%
Other values (46)46
82.1%

Most occurring characters

ValueCountFrequency (%)
/224
 
10.3%
t168
 
7.7%
p168
 
7.7%
s168
 
7.7%
e168
 
7.7%
a112
 
5.1%
i112
 
5.1%
.112
 
5.1%
m112
 
5.1%
o112
 
5.1%
Other values (16)728
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1400
64.1%
Other Punctuation392
 
17.9%
Decimal Number392
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t168
12.0%
p168
12.0%
s168
12.0%
e168
12.0%
a112
8.0%
i112
8.0%
m112
8.0%
o112
8.0%
h56
 
4.0%
d56
 
4.0%
Other values (3)168
12.0%
Decimal Number
ValueCountFrequency (%)
283
21.2%
954
13.8%
146
11.7%
045
11.5%
334
8.7%
828
 
7.1%
427
 
6.9%
726
 
6.6%
625
 
6.4%
524
 
6.1%
Other Punctuation
ValueCountFrequency (%)
/224
57.1%
.112
28.6%
:56
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1400
64.1%
Common784
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/224
28.6%
.112
14.3%
283
 
10.6%
:56
 
7.1%
954
 
6.9%
146
 
5.9%
045
 
5.7%
334
 
4.3%
828
 
3.6%
427
 
3.4%
Other values (3)75
 
9.6%
Latin
ValueCountFrequency (%)
t168
12.0%
p168
12.0%
s168
12.0%
e168
12.0%
a112
8.0%
i112
8.0%
m112
8.0%
o112
8.0%
h56
 
4.0%
d56
 
4.0%
Other values (3)168
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/224
 
10.3%
t168
 
7.7%
p168
 
7.7%
s168
 
7.7%
e168
 
7.7%
a112
 
5.1%
i112
 
5.1%
.112
 
5.1%
m112
 
5.1%
o112
 
5.1%
Other values (16)728
33.3%

Interactions

2022-05-09T21:03:20.277518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:59.397075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:04.409198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:06.666118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:08.801437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:11.147372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:14.421213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:16.507092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:18.279259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:20.987965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:01.000132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:05.148137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:07.571017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:09.532685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:12.004539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:15.146764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:17.141787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:18.959635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:21.121987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:01.453831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:05.275181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:07.736583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:09.673960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:12.323172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:15.296667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:17.263798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:19.229499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:21.225520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:01.876557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:05.422387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:07.881393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:09.855001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:12.574446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:15.462053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:17.376622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:19.346316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:21.357964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:02.220633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:05.703639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:08.020470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:10.036786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:12.836731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:15.623312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:17.531179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:19.477449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:21.611902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:02.696145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:06.038378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:08.265937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:10.492337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:13.269597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:15.924360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:17.763728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:19.761791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:21.737415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:02.978119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:06.196074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:08.372439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:10.656911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:13.503279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:16.099551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:17.890934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:19.888748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:21.845900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:03.384620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:06.397146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:08.518098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:10.820159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:13.771268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:16.235063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:18.001444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:20.036905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:21.957753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:03.876727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:06.537392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:08.666418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:11.011289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:14.139986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:16.372754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:18.142344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:03:20.168937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:03:30.139949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:03:30.261270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:03:30.402174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:03:30.559329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:03:30.811007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:03:22.217422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:03:23.218446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:03:23.601171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:03:23.745034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
02269800https://www.tvmaze.com/episodes/2269800/manaki-1x07-golos-zvera-delo-arhangelskogo-manakaГолос зверя. Дело архангельского маньяка1.07.0regular2020-12-06nan2020-12-06T00:00:00+00:0040.0nan48597https://www.tvmaze.com/shows/48597/manakiМаньякиDocumentaryRussian[]Running40.040.02020-05-22nanhttps://premier.one/show/1242015.0nan<p>The non-fiction series "Maniacs" tells about the most high-profile Russian crimes, in which still not all the circumstances are clear to the end</p>1.643899e+09https://api.tvmaze.com/episodes/1977902
11956338https://www.tvmaze.com/episodes/1956338/hero-return-1x09-episode-9Episode 91.09.0regular2020-12-0610:002020-12-06T02:00:00+00:0015.0nan51471https://www.tvmaze.com/shows/51471/hero-returnHero ReturnAnimationChinese['Action', 'Anime', 'Science-Fiction']Running15.016.02020-10-18nanhttps://v.qq.com/detail/q/q72jd29a3oxflsr.html76.0nan<p>Zero was mankind's first real superhero. Under his watch, countless other superheros appeared and followed in his footsteps. However, after 5 years of war, Zero disappeared without a trace.<br /><br />(Source: zeroscans)</p>1.603467e+09https://api.tvmaze.com/episodes/2015818
21978217https://www.tvmaze.com/episodes/1978217/swallowed-star-1x03-episode-3Episode 31.03.0regular2020-12-0610:002020-12-06T02:00:00+00:00NaNnan52178https://www.tvmaze.com/shows/52178/swallowed-starSwallowed StarAnimationChinese['Anime', 'Science-Fiction']RunningNaN21.02020-11-29nanhttps://v.qq.com/detail/3/324olz7ilvo2j5f.html88.0nan<p>One day, an unexplained RR virus appeared on the earth, drawing the world into disaster. Infected animals mutated into terrible monsters, invaded massively, and humans built walls around the destruction and established the base city as the last bastion for humans. The suffering that mankind has experienced during this period of time is known as the "Great Nirvana Period." Not only that, Luo Feng not only carried the burden of supporting the family but also to protect the human homeland, for the better survival and development of mankind, together with other justice warriors, to join hands against the fierce monsters. Under the desperate situation of the end, can Luo Feng and other warriors repel monsters and successfully protect the human world?</p>1.648371e+09https://api.tvmaze.com/episodes/1964000
32052506https://www.tvmaze.com/episodes/2052506/wu-shen-zhu-zai-1x81-episode-81Episode 811.081.0regular2020-12-0610:002020-12-06T02:00:00+00:008.0nan54033https://www.tvmaze.com/shows/54033/wu-shen-zhu-zaiWu Shen Zhu ZaiAnimationChinese['Action', 'Adventure', 'Anime', 'Fantasy']Running8.08.02020-03-08nanhttps://v.qq.com/detail/m/7q544xyrava3vxf.html76.0nan<p>The protagonist Qin Chen, who was originally the top genius in the military domain, was conspired by the people to fall into the death canyon in the forbidden land of the mainland. Qin Chen, who was inevitably dead, unexpectedly triggered the power of the mysterious ancient sword.<br /><br />Three hundred years later, in a remote part of the Tianwu mainland, a boy of the same name accidentally inherited Qin Chen's will. As the beloved grandson of King Dingwu of the Daqi National Army, due to the birth father's birth, the mother and son were treated coldly in Dingwu's palace and lived together. In order to rewrite the myth of the strong man in hope of the sun, and to protect everything he loves, Qin Chen resolutely took up the responsibility of maintaining the five kingdoms of the world and set foot on the road of martial arts again.</p>1.649423e+09https://api.tvmaze.com/episodes/1995405
42138924https://www.tvmaze.com/episodes/2138924/tokyo-joshi-pro-wrestling-2020-12-06-tjpw-fall-tour-20-womm-wrestling-of-my-mindTJPW Fall Tour '20 ~ WOMM (Wrestling Of My Mind) ~2020.042.0regular2020-12-0612:002020-12-06T03:00:00+00:00120.0nan49740https://www.tvmaze.com/shows/49740/tokyo-joshi-pro-wrestlingTokyo Joshi Pro WrestlingSportsJapanese[]Running120.0120.02013-01-30nanhttps://www.ddtpro.com/13.0nannan1.651764e+09https://api.tvmaze.com/episodes/2007760
52012320https://www.tvmaze.com/episodes/2012320/mans-diary-2x05-episode-5Episode 52.05.0regular2020-12-06nan2020-12-06T04:00:00+00:0012.0nan50398https://www.tvmaze.com/shows/50398/mans-diaryMan's DiaryAnimationChinese['Anime', 'Supernatural']Running12.012.02019-07-21nanhttps://www.bilibili.com/bangumi/media/md43146223.0nan<p>In the twenty-first century, gods and demons can no longer maintain balance due to the rapid development of human society. In an effort to restore proper order, the gods began to take care of saving the world, for which they sent a group of gods and monsters to the world of people, who must find there the " key " to salvation. Su moting is a girl with the personality of "demon child". When her parents asked her to leave home so that she could become independent and independent, she met the beautiful and charming God of Tianjin and the mysterious demon cat. So begins a new turbulent round of su moting's life.</p><p><br /> </p>1.611039e+09https://api.tvmaze.com/episodes/1985789
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